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Labeled data can be costly or impractical to obtain, making fully supervised training difficult.
Motivation for weak supervision (semi-supervised learning) is that obtaining labeled data is often expensive, time-consuming, or impractical because it may require skilled human effort (e.g., transcription) or costly physical experimentation (e.g., determining protein structure or detecting oil). As a result, large fully labeled training sets may be infeasible, while collecting unlabeled data is comparatively cheap. Weak supervision addresses this by training models using a small subset of human-labeled (or otherwise precisely labeled) examples together with a much larger set of unlabeled (or imprecisely labeled) examples. The goal is to improve predictive performance beyond what would be achieved by either (1) discarding the unlabeled data and training only on the limited labeled set (supervised learning) or (2) discarding the labels and relying only on unlabeled data (unsupervised learning).
Labeled data can be costly or impractical to obtain, making fully supervised training difficult.
Unlabeled data is cheaper to collect, enabling training with far fewer labels.
Weak supervision combines small labeled data with large unlabeled data to outperform purely supervised or purely unsupervised approaches.
A machine learning paradigm that uses a small amount of labeled data together with a large amount of unlabeled (or imprecisely labeled) data to improve performance.
The expense and effort required to obtain accurate labels, which can limit the size of fully labeled training datasets.
Input examples without known target outputs, which are cheaper to acquire and can still help learning in semi-supervised settings.
“Can you explain what "Labeled data can be costly or impractical to obtain, making fully supervised training difficult." means in simple terms?”